Editing Readability (section)

== History ==

=== Early research ===
For centuries, teachers and educators have seen the importance of organization, coherence, and emphasis in good writing. In the 1880s, English professor L. A. Sherman found that the English sentence was getting shorter. In [[Elizabethan]] times, the average sentence was 50 words long while in Sherman's modern time, it was 23 words long.

Sherman's work established that:

* Literature is a subject for statistical analysis.
* Shorter sentences and concrete terms help people to make sense of what is written.
* Speech is easier to understand than text.
* Over time, text becomes easier if it is more like speech.

Sherman wrote: "No man should talk worse than he writes, no man should write better than he should talk..." He wrote this wanting to emphasize that the closer writing is to speech, the more clear and effective the content becomes.<ref name="Sherman2">Sherman, Lucius Adelno 1893. ''Analytics of literature: A manual for the objective study of English prose and poetry''. Boston: Ginn and Co.</ref>

In 1889 in Russia, the writer Nikolai A. Rubakin published a study of over 10,000 texts written by everyday people.<ref name="Choldin" /> From these texts, he took 1,500 words he thought most people understood. He found that the main blocks to comprehension are ''unfamiliar words'' and ''long sentences''.<ref name="Lorge1944a">Lorge, I. 1944. "Word lists as background for communication." ''Teachers College Record'' 45:543–552.</ref> Starting with his own journal at the age of 13, Rubakin published many articles and books on science and many subjects for the great numbers of new readers throughout Russia. In Rubakin's view, the people were not fools. They were simply poor and in need of cheap books, written at a level they could grasp.<ref name="Choldin">{{Citation | last = Choldin | first = M.T. |author-link=Marianna Tax Choldin| contribution = Rubakin, Nikolai Aleksandrovic | year = 1979 | title = Encyclopedia of library and information science | editor-last = Kent | editor-first = Allen | editor2-last = Lancour | editor2-first = Harold | editor3-last = Nasri | editor3-first = William Z. | editor4-last = Daily | editor4-first = Jay Elwood | volume = 26 | pages = 178–179 | publisher = CRC Press | edition = illustrated | isbn = 978-0-8247-2026-1 | url = https://books.google.com/books?id=hr1RHr8lRcUC&pg=PA178}}</ref>

=== Reading ease ===

The earliest reading ease assessment is the subjective judgment termed '''text leveling'''.  Formulas do not fully address the various content, purpose, design, visual input, and organization of a text.<ref name="Clay">Clay, M. 1991. ''Becoming literate: The construction of inner control.''  Portsmouth, NH: Heinneman.</ref><ref name="frylevel">Fry, E. B. 2002. "Text readability versus leveling." ''Reading Teacher'' 56 no. 23:286–292.</ref><ref>{{Cite journal |last1=Crossley |first1=S |last2=Heintz |first2=A |last3=Choi |first3=J |last4=Batchler |first4=J |last5=Karimi |first5=M |last6=Malatinszky |first6=A |date=2023 |title=A large-scaled corpus for assessing text readability |journal=Behavior Research Methods |volume=55 |issue=2 |pages=491–507|doi=10.3758/s13428-022-01802-x |pmid=35297016 |pmc=10027808 }}</ref> Text leveling is commonly used to rank the reading ease of texts in areas where reading difficulties are easy to identify, such as books for young children. At higher levels, ranking reading ease becomes more difficult, as individual difficulties become harder to identify. This has led to better ways to assess reading ease.

In the 1920s, the scientific movement in education looked for tests to measure students' achievement to aid in curriculum development. Teachers and educators had long known that, to improve reading skill, readers—especially beginning readers—need reading material that closely matches their ability. University-based psychologists did much of the early research, which was later taken up by textbook publishers.<ref name="fry">Fry, Edward B. 2006. "Readability." ''Reading Hall of Fame Book.'' Newark, DE: International Reading Assn.</ref> In 1921, Harry D. Kitson published ''The Mind of the Buyer'', one of the first books to apply psychology to marketing. Kitson's work showed that each type of reader bought and read their own type of text. On reading two newspapers and two magazines, he found that short sentence length and short [[word length]] were the best contributors to reading ease.<ref name="Kitson">Kitson, Harry D. 1921. ''The Mind of the Buyer.'' New York: Macmillan.</ref>

In 1923, Bertha A. Lively and [[Sidney L. Pressey]] published the first reading ease formula. They were concerned that junior high school science textbooks had so many technical words and that teachers would spend all class time explaining these words. They argued that their formula would help to measure and reduce the "vocabulary burden" of textbooks. Their formula used five variable inputs and six constants. For each thousand words, it counted the number of unique words, the number of words not on the Thorndike list, and the median index number of the words found on the list. Manually, it took three hours to apply the formula to a book.<ref name="Lively">Lively, Bertha A. and S. L. Pressey. 1923. "A method for measuring the 'vocabulary burden' of textbooks. ''Educational administration and supervision'' 9:389–398.</ref>

After the Lively–Pressey study, people looked for formulas that were more accurate and easier to apply. In 1928, Carleton Washburne and Mabel Vogel created the first modern readability formula. They validated it by using an outside criterion, and correlated .845 with test scores of students who read and liked the criterion books.<ref name="DuBay2">[https://files.eric.ed.gov/fulltext/ED506404.pdf The Classic Readability Studies, William H. DuBay, Editor (chapter on Washburne, C. i M. Vogel. 1928).]</ref> It was also the first to introduce the variable of interest to the concept of readability.<ref name="Washburne">Washburne, C. and M. Vogel. 1928. "An objective method of determining grade placement of children's reading material. ''Elementary school journal'' 28:373–81.</ref>

Between 1929 and 1939, Alfred Lewerenz of the Los Angeles School District published several new formulas.<ref name="Lewerenz1929a">Lewerenz, A. S.  1929. "Measurement of the difficulty of reading materials." ''Los Angeles educational research bulletin'' 8:11–16.</ref><ref name="Lewerenz1929b">Lewerenz, A. S. 1929. "Objective measurement of diverse types of reading material. ''Los Angeles educational research bulletin'' 9:8–11.</ref><ref name="Lewerenz1930">Lewerenz, A. S.  1930. "Vocabulary grade placement of typical newspaper content." ''Los Angeles educational research bulletin'' 10:4–6.</ref><ref name="Lewerenz1935">Lewerenz, A. S.  1935. "A vocabulary grade placement formula." ''Journal of experimental education'' 3: 236</ref><ref name="Lewerenz1939">Lewerenz, A. S. 1939. "Selection of reading materials by pupil ability and interest." ''Elementary English review'' 16:151–156.</ref>

In 1934, educational psychologist [[Edward Thorndike]] of Columbia University noted that, in Russia and Germany, teachers used word frequency counts to match books to students. Word skill was the best sign of intellectual development, and the strongest predictor of reading ease. In 1921, Thorndike published ''Teachers Word Book'', which contained the [[word frequency|frequencies]] of 10,000 words.<ref>Thorndike E.L. 1921 ''The teacher's word book''. 1932 ''A teacher's word book of the twenty thousand words found most frequently and widely in general reading for children and young people''. 1944 (with J.E. Lorge) ''The teacher's word book of 30,000 words''.</ref> He also published his readability formula. He wrote that word skills can be increased if the teacher introduces new words and repeats them often.<ref name="Thorn2">Thorndike, E. 1934. "Improving the ability to read." ''Teachers college record'' 36:1–19, 123–44, 229–41. October, November, December.</ref> In 1939, W.W. Patty and W. I Painter published a formula for measuring the vocabulary burden of textbooks. This was the last of the early formulas that used the Thorndike vocabulary-frequency list.<ref name="Patty">Patty. W. W. and W. I. Painter. 1931. "A technique for measuring the vocabulary burden of textbooks." ''Journal of educational research'' 24:127–134.</ref> Until computers came along, word frequency lists were the best aids for grading reading ease of texts.<ref name="KlareBuck3">Klare, G. R. and B. Buck. 1954. ''Know Your Reader: The scientific approach to readability.'' New York: Heritage House.</ref>

In 1981 the World Book Encyclopedia listed the grade levels of 44,000 words.<ref name="livingword">Dale, E. and J. O'Rourke. 1981. ''The living word vocabulary: A national vocabulary inventory.'' World Book-Childcraft International.</ref> A popular strategy amongst educators in modern times is "incidental vocabulary learning," which enforces efficiency in learning vocabulary in the short-term rather than drilling words and meanings teachers hope will stick.<ref>{{Cite journal |last=He |first=Shumin 1 1 Country Garden Experimental School |date=2023 |title=Exploration of Incidental Vocabulary Learning Strategies from Different Modes to Acquire Vocabulary |journal=The Educational Review |volume=7 |issue=7 |language=English |pages=927–932 |doi=10.26855/er.2023.07.014|id={{ProQuest|2866467078}} |doi-access=free }}</ref> The incidental learning tactic is meant to help learners build comprehension and learning skills rather than memorizing words. Through this strategy, students would hopefully be able to navigate various levels of readability using context clues and comprehension.

=== Readability studies ===

During the recession of the 1930s, the U.S. government invested in [[adult education]]. In 1931, [[Douglas Waples]] and [[Ralph W. Tyler|Ralph Tyler]] published ''What Adults Want to Read About.'' It was a two-year study of adult reading interests. Their book showed not only what people read but what they would like to read. They found that many readers lacked suitable reading materials: they would have liked to learn but the reading materials were too hard for them.<ref name="Waples">Waples, D. and R. Tyler. 1931. ''What adults want to read about.''Chicago: University of Chicago Press.</ref>

[[Lyman Bryson]] of [[Teachers College, Columbia University]] found that many adults had poor reading ability due to poor education. Even though [[college]]s  had long tried to teach how to write in a clear and readable style, Bryson found that it was rare. He wrote that such language is the result of a "...[[discipline]] and artistry that few people who have ideas will take the trouble to achieve... If simple language were easy, many of our problems would have been solved long ago."<ref name="KlareBuck3" /> Bryson helped set up the Readability Laboratory at the college. Two of his students were Irving Lorge and [[Rudolf Flesch]].

In 1934, Ralph Ojemann investigated adult reading skills, factors that most directly affect reading ease, and causes of each level of difficulty. He did not invent a formula, but a method for assessing the difficulty of materials for [[parent education]]. He was the first to assess the validity of this method by using 16 magazine passages tested on actual readers. He evaluated 14 measurable and three reported factors that affect reading ease.

Ojemann emphasized the reported features, such as whether the text was coherent or unduly abstract. He used his 16 passages to compare and judge the reading ease of other texts, a method now called ''scaling''. He showed that even though these factors cannot be measured, they cannot be ignored.<ref name="Ojemann">Ojemann, R. H. 1934. "The reading ability of parents and factors associated with reading difficulty of parent-education materials." ''University of Iowa studies in child welfare'' 8:11–32.</ref>

Also in 1934, [[Ralph W. Tyler|Ralph Tyler]] and [[Edgar Dale]] published the first adult reading ease formula based on passages on health topics from a variety of textbooks and magazines. Of 29 factors that are significant for young readers, they found ten that are significant for adults. They used three of these in their formula.<ref name="DaleTyler">Dale, E. and R. Tyler. 1934. "A study of the factors influencing the difficulty of reading materials for adults of limited reading ability." ''Library quarterly'' 4:384–412.</ref>

In 1935, [[William S. Gray]] of the [[University of Chicago]] and Bernice Leary of [[Saint Xavier University|Xavier College in Chicago]] published ''What Makes a Book Readable,'' one of the most important books in readability research. Like Dale and Tyler, they focused on what makes books readable for adults of limited reading ability. Their book included the first scientific study of the reading skills of American adults. The sample included 1,690 adults from a variety of settings and regions. The test used a number of passages from [[newspaper]]s, magazines, and books—as well as a standard reading test. They found a mean grade score of 7.81 (eighth month of the [[seventh grade]]). About one-third read at the 2nd to 6th-[[grade level]], one-third at the 7th to 12th-grade level, and one-third at the 13th–17th grade level. The authors emphasized that one-half of the adult population at that time lacked suitable reading materials. They wrote, "For them, the enriching values of reading are denied unless materials reflecting adult interests are adapted to their needs." The poorest readers, one-sixth of the adult population, need "simpler materials for use in promoting functioning [[literacy]] and in establishing fundamental reading habits."<ref name="Gray">Gray, W. S. and B. Leary. 1935. ''What makes a book readable''. Chicago: Chicago University Press.</ref>

In 1939, Irving Lorge published an article that reported other combinations of variables that indicate difficulty more accurately than the ones Gray and Leary used. His research also showed that, "The vocabulary load is the most important concomitant of difficulty."<ref name="Lorge1939">Lorge, I. 1939. "Predicting reading difficulty of selections for children. ''Elementary English Review'' 16:229–233.</ref> In 1944, Lorge published his ''Lorge Index'', a readability formula that used three variables and set the stage for simpler and more reliable formulas that followed.<ref name="Lorge1944b">Lorge, I. 1944. "Predicting readability." ''Teachers college record'' 45:404–419.</ref>

By 1940, investigators had:
* Successfully used statistical methods to analyze reading ease
* Found that unusual words and sentence length were among the first causes of reading difficulty
* Used vocabulary and sentence length in formulas to predict reading ease

=== Readership formula adoption ===

In 1943, Rudolf Flesch published his PhD dissertation, ''Marks of a Readable Style'', which included a readability formula to predict the difficulty of adult reading material. Investigators in many fields began using it to improve communications. One of the variables it used was ''personal references,'' such as names and personal pronouns. Another variable was ''affixes''.<ref name="FleschStyle">Flesch, R. "Marks of a readable style." ''Columbia University contributions to education,'' no. 187. New York: Bureau of Publications, Teachers College, Columbia University.</ref>

In 1947, Donald Murphy of ''Wallace's Farmer'' used a split-run<ref name="Murphy2">Murphy, D. 1947. "How plain talk increases readership 45% to 60%." ''Printer's ink.'' 220:35–37.</ref> edition to study the effects of making text easier to read. He found that reducing from the 9th to the 6th-grade reading level increased readership by 43% for an article about 'nylon'.  He also found a 60% increase in readership for an article on corn, with better responses from people under 35.<ref name="Murphy2" /> The result was a gain of 42,000 readers in a circulation of 275,000.

Wilber Schramm, who directed the Communications Research program at the University of Illinois interviewed 1,050 newspaper readers in 1947. He found that an easier reading style helps to determine how much of an article is read. This was called reading persistence, depth, or perseverance He also found that people will read less of long articles than of short ones, for example, a story nine paragraphs long will lose 3 out of 10 readers by the fifth paragraph. In contrast, a shorter story will lose only 2 out of 10 readers.<ref name="Schramm">Schramm, W. 1947. "Measuring another dimension of newspaper readership." ''Journalism quarterly'' 24:293–306.</ref>

A study in 1947 by Melvin Lostutter showed that newspapers were generally written at a level five years above the ability of average American adult readers. The reading ease of newspaper articles was not found to have much connection with the education, experience, or personal interest of the journalists writing the stories. It instead had more to do with the convention and culture of the industry. Lostutter argued for more readability testing in newspaper writing. Improved readability must be a "conscious process somewhat independent of the education and experience of the staffs ''writers.''"''<ref name="Lostutter">Lostutter, M. 1947. "Some critical factors in newspaper readability." ''Journalism quarterly'' 24:307–314.</ref>''

In 1948, Flesch published his [[Flesch–Kincaid readability tests|Reading Ease]] formula in two parts. Rather than using grade levels, it used a scale from 0 to 100, with 0 equivalent to the 12th grade and 100 equivalent to the 4th grade. It dropped the use of affixes. The second part of the formula predicts human interest by using personal references and the number of personal sentences. The new formula correlated 0.70 with the McCall-Crabbs reading tests.<ref name="FleschEase">Flesch, R. 1948. "A new readability yardstick." ''Journal of Applied Psychology'' 32:221–33.</ref>

In 1948, Bernard Feld did a study of every item and ad in the ''Birmingham News'' of 20 November 1947. He divided the items into those above the 8th-grade level and those at the 8th grade or below. He chose the 8th-grade breakpoint, as that was determined to be the average reading level of adult readers. An 8th-grade text "...will reach about 50% of all American grown-ups," he wrote. Among the wire-service stories, the lower group got two-thirds more readers, and among local stories, 75% more readers. Feld also believed in drilling writers in Flesch's clear-writing principles.<ref name="Feld">Feld, B. 1948. "Empirical test proves clarity adds readers." ''Editor and publisher'' 81:38.</ref>

Both Rudolf Flesch and Robert Gunning worked extensively with newspapers and the wire services in improving readability. Mainly through their efforts in a few years, the readability of US newspapers went from the 16th to the 11th-grade level, where it remains today. Publishers discovered that the Flesch formulas could increase readership up to 60%. Flesch's work made an enormous impact on journalism. The Flesch Reading Ease formula became one of the most widely used, tested, and reliable readability metrics.<ref name="Klare63">Klare, G. R. 1963. ''The measurement of readability''. Ames, Iowa: University of Iowa Press.</ref><ref name="Chall">Chall, J. S. 1958. ''Readability: An appraisal of research and application.'' Columbus, OH: Bureau of Educational Research, Ohio State University.</ref> In 1951, Farr, Jenkins, and Patterson simplified the formula further by changing the syllable count.<ref name="Farr" />

=== Formula refinement and variants ===

In the 1940s, Robert Gunning helped bring readability research into the workplace. In 1944, he founded the first readability consulting firm dedicated to reducing the "fog" in newspapers and business writing. In 1952, he published ''The Technique of Clear Writing'' with his own Fog Index, a formula that correlates 0.91 with comprehension as measured by reading tests.<ref name="DuBay" />

[[Edgar Dale]], a professor of education at Ohio State University, was one of the first critics of Thorndike's vocabulary-frequency lists. He claimed that they did not distinguish between the different meanings that many words have. He created two new lists of his own. One, his "short list" of 769 easy words, was used by Irving Lorge in his formula. The other was his "long list" of 3,000 easy words, which were understood by 80% of fourth-grade students. However, one has to extend the word lists by regular plurals of nouns, regular forms of the past tense of verbs, progressive forms of verbs etc. In 1948, he incorporated this list into a formula he developed with [[Jeanne Chall|Jeanne S. Chall]], who later founded the Harvard Reading Laboratory. In 1995, Dale and Chall published a new version of their formula with an upgraded word list, the New Dale–Chall readability formula.<ref name="Dale-Chall2">Chall, J. S. and E. Dale. 1995. ''Readability revisited: The new Dale–Chall readability formula.'' Cambridge, MA: Brookline Books.</ref>

The [[Spache readability formula]] was developed in 1952.

In 1963, while teaching English teachers in Uganda, Edward Fry developed his [[Fry readability formula|Readability Graph]]. It became one of the most popular formulas and easiest to apply.<ref name="Fry">Fry, E. B. 1963. ''Teaching faster reading''. London: Cambridge University Press.</ref><ref name="Fry2">Fry, E. B. 1968. "A readability formula that saves time." '' Journal of reading '' 11:513–516.</ref>

The [[automated readability index]] was developed in 1967.

Harry McLaughlin  determined that word length and sentence length should be multiplied rather than added as in other formulas. In 1969, he published his SMOG (Simple Measure of Gobbledygook) formula. It is often recommended for use in healthcare.<ref name="Doak">Doak, C. C., L. G. Doak, and J. H. Root. 1996. ''Teaching patients with low literacy skills''. Philadelphia: J. B. Lippincott & Co.</ref>

The Golub Syntactic Density Score was developed by Lester Golub in 1974.{{cn|date=September 2024}}

In 1973, a study commissioned by the US military of the reading skills required for different military jobs produced the FORCAST formula. Unlike most other formulas, it uses only a vocabulary element, making it useful for texts without complete sentences. The formula satisfied requirements that it would be:
* Based on Army-job reading materials.
* Suitable for the young adult-male recruits.
* Easy enough for Army clerical personnel to use without special training or equipment.<ref name="forcast" />

In 1975, in a project sponsored by the U.S. Navy, the Reading Ease formula was recalculated to give a grade-level score. The new formula is now called the [[Flesch–Kincaid readability tests|Flesch–Kincaid grade-level]] formula.<ref name="Kincaid">Kincaid, J. P., R. P. Fishburne, R. L. Rogers, and B. S. Chissom. 1975. ''Derivation of new readability formulas (Automated Readability Index, Fog Count, and Flesch Reading Ease Formula) for Navy enlisted personnel.'' CNTECHTRA Research Branch Report 8-75.</ref>

The [[Linsear Write]] Raygor readability estimate was developed in 1977.

In 1978, John Bormuth of the University of Chicago looked at reading ease using the new [[Cloze test|Cloze deletion test]] developed by Wilson Taylor. His work supported earlier research including the degree of reading ease for each kind of reading. The best level for classroom "assisted reading" is a slightly difficult text that causes a "set to learn", and for which readers can correctly answer 50% of the questions of a multiple-choice test. The best level for unassisted reading is one for which readers can correctly answer 80% of the questions. These cutoff scores were later confirmed by Vygotsky<ref name="Vygotsky">Vygotsky, L. 1978. ''Mind in society.'' Cambridge, MA: Harvard University Press.</ref> and Chall and Conard.<ref name="ChallConard">Chall, J. S. and S. S. Conard. 1991. ''Should textbooks challenge students? The case for easier or harder textbooks.'' New York: Teachers College Press.</ref>
Among other things, Bormuth confirmed that vocabulary and sentence length are the best indicators of reading ease. He showed that the measures of reading ease worked as well for adults as for children. The same things that children find hard are the same for adults of the same reading levels. He also developed several new measures of cutoff scores. One of the most well known was the ''Mean Cloze Formula'', which was used in 1981 to produce the ''Degree of Reading Power'' system used by the College Entrance Examination Board.<ref name="Bormuth">Bormuth, J. R. 1966. "Readability: A new approach." ''Reading research quarterly 1:79–132.''</ref><ref name="Bormuth2">Bormuth, J. R. 1969. ''Development of readability analysis'': Final Report, Project no 7-0052, Contract No. OEC-3-7-0070052-0326. Washington, D. C.: U. S. Office of Education, Bureau of Research, U. S. Department of Health, Education, and Welfare.</ref><ref name="Bormuth3">Bormuth, J. R. 1971. ''Development of standards of readability: Towards a rational criterion of passage performance.'' Washington, D. C.: U. S. Office of Education, Bureau of Research, U. S. Department of Health, Education, and Welfare.</ref>

In 1988, Jack Stenner and his associates at MetaMetrics, Inc. published the [[Lexile]] Framework for assessing readability and matching students with appropriate texts. The Lexile framework uses average sentence length, and average word frequency in the American Heritage Intermediate Corpus to predict a score on a 0–2000 scale. The AHI Corpus includes five million words from 1,045 published works often read by students in grades three to nine.{{Citation needed|date=February 2024}}<!--what's this trying to say?-->

In 2000, researchers of the School Renaissance Institute and Touchstone Applied Science Associates published their Advantage-TASA Open Standard (ATOS) Reading ease Formula for Books. They worked on a formula that was easy to use and that could be used with any  texts.

The project was one of the widest reading ease projects ever. The developers of the formula used 650 normed reading texts, 474 million words from all the text in 28,000 books read by students. The project also used the reading records of more than 30,000 who read and were tested on 950,000 books.

They found that three variables give the most reliable measure of text reading ease:

*words per sentence
*average grade level of words
*characters per word

They also found that:

*To help learning, the teacher should match book reading ease with reading skill.
*Reading often helps with reading gains.
*For reading alone below the 4th grade, the best learning gain requires at least 85% comprehension.
*Advanced readers need 92% comprehension for independent reading.
*Book length can be a good measure of reading ease.
*Feedback and interaction with the teacher are the most important factors in reading.<ref name="atos">School Renaissance Institute. 2000. ''The ATOS readability formula for books and how it compares to other formulas.'' Madison, WI: School Renaissance Institute, Inc.</ref><ref name="Paul">Paul, T. 2003. ''Guided independent reading.'' Madison, WI: School Renaissance Institute, Inc. [http://www.renlearn.com/GIRP2008.pdf  http://www.renlearn.com/GIRP2008.pdf]</ref>

=== Measuring coherence and organization ===
Beginning in the 1970s, cognitive theorists began teaching that reading is really an act of thinking and organization. The reader constructs meaning by mixing new knowledge into existing knowledge. Because of the limits of the reading ease formulas, some research looked at ways to measure the content, organization, and coherence of text. Although this did not improve the reliability of the formulas, their efforts showed the importance of these variables in reading ease.

Studies by [[Walter Kintch]] and others showed the central role of coherence in reading ease, mainly for people learning to read.<ref name="Kintsch">Kintsch, W. and J. R. Miller 1981. "Readability: A view from cognitive psychology." In ''Teaching: Research reviews.'' Newark, DE: International Reading Assn.</ref> In 1983, Susan Kemper devised a formula based on physical states and mental states. However, she found this was no better than word familiarity and sentence length in showing reading ease.<ref name="Kemper">Kemper, S. 1983. "Measuring the inference load of a text." ''Journal of educational psychology'' 75, no. 3:391–401.</ref>

Bonnie Meyer and others tried to use organization as a measure of reading ease. While this did not result in a formula, they showed that people read faster and retain more when the text is organized into topics. She found that a visible plan for presenting content greatly helps readers to assess a text. A hierarchical plan shows how the parts of the text are related. It also aids the reader in blending new information into existing knowledge structures.<ref name="Meyer">Meyer, B. J. 1982. "Reading research and the teacher: The importance of plans." ''College composition and communication'' 33, no. 1:37–49.</ref>

Bonnie Armbruster found that the most important feature for learning and comprehension is textual coherence, which comes in two types:
*Global coherence, which integrates high-level ideas as themes in an entire section, chapter, or book.
*Local coherence, which joins ideas within and between sentences.

Armbruster confirmed Kintsch's finding that coherence and structure are more help for younger readers.<ref name="Armbruster">Armbruster, B. B. 1984. "The problem of inconsiderate text" In ''Comprehension instruction'', ed. G. Duffy. New York: Longmann, p. 202–217.</ref> R. C. Calfee and R. Curley built on Bonnie Meyer's work and found that an unfamiliar underlying structure can make even simple text hard to read. They brought in a graded system to help students progress from simpler story lines to more advanced and abstract ones.<ref name="Calfee">Calfee, R. C. and R. Curley. 1984. "Structures of prose in content areas." In ''Understanding reading comprehension'', ed. J. Flood. Newark, DE: International Reading Assn., pp. 414–430.</ref>

Many other studies looked at the effects on reading ease of other text variables, including:
* Image words, abstraction, direct and indirect statements, types of narration and sentences, phrases, and clauses;<ref name="Gray" />
* Difficult concepts;<ref name="Chall" />
* Idea density;<ref name="Dolch">Dolch. E. W. 1939. "Fact burden and reading difficulty." ''Elementary English review'' 16:135–138.</ref>
* Human interest;<ref name="Gunning2" /><ref name="Fleschwrite">{{cite book |last=Flesch |first=R. |author-link=Rudolf Flesch |year=1949 |title=The Art of Readable Writing |location=New York |publisher=Harper |oclc=318542}}</ref>
* Nominalization;<ref name="ColemanBlu">Coleman, E. B. and P. J. Blumenfeld. 1963. "Cloze scores of nominalization and their grammatical transformations using active verbs." ''Psychology reports'' 13:651–654.</ref>
* Active and passive voice;<ref name="Gough">Gough, P. B. 1965. "Grammatical transformations and the speed of understanding." ''Journal of verbal learning and verbal behavior'' 4:107–111.</ref><ref name="Coleman">Coleman, E. B. 1966. "Learning of prose written in four grammatical transformations." ''Journal of Applied Psychology'' 49:332–341.</ref><ref name="Clark">Clark, H. H. and S. E. Haviland. 1977. "Comprehension and the given-new contract." In ''Discourse production and comprehension,'' ed. R. O. Freedle. Norwood, NJ: Ablex Press, pp. 1–40.</ref><ref name="Hornby">Hornby, P. A. 1974. "Surface structure and presupposition." ''Journal of verbal learning and verbal behavior'' 13:530–538.</ref>
* Embeddedness;<ref name="Coleman" />
* Structural cues;<ref name="Spyridakis">Spyridakis, J. H. 1989. "Signaling effects: A review of the research-Part 1." ''Journal of technical writing and communication'' 19, no 3:227-240.</ref><ref name="Spyri2">Spyridakis, J. H. 1989. "Signaling effects: Increased content retention and new answers-Part 2." ''Journal of technical writing and communication'' 19, no. 4:395–415.</ref>
* The use of images;<ref name="Halbert">Halbert, M. G. 1944. "The teaching value of illustrated books." ''American school board journal'' 108, no. 5:43–44.</ref><ref name="Vernon">Vernon, M. D. 1946. "Learning from graphic material." ''British journal of psychology'' 36:145–158.</ref>
* Diagrams and line graphs;<ref name="Felker">Felker, D. B., F. Pickering, V. R. Charrow, V. M. Holland, and J. C. Redish. 1981. ''Guidelines for document designers.'' Washington, D. C: American Institutes for Research.</ref>
* Highlighting;<ref name="Klarehigh">Klare, G. R., J. E. Mabry, and L. M. Gustafson. 1955. "The relationship of patterning (underlining) to immediate retention and to acceptability of technical material." ''Journal of Applied Psychology'' 39, no 1:40–42.</ref>
*Fonts and layout;<ref name="Klaretypo">Klare, G. R. 1957. "The relationship of typographic arrangement to the learning of technical material." ''Journal of Applied Psychology'' 41, no 1:41–45.</ref>
*Document age.<ref name="Jatowt">Jatowt, A. and K. Tanaka. 2012. "Longitudinal analysis of historical texts' readability." ''Proceedings of Joint Conference on Digital Libraries 2012'' 353-354</ref>

[[Coh-Metrix]] can be used in many different ways to investigate the cohesion of the explicit text and the coherence of the mental representation of the text. "Our definition of [[Cohesion (linguistics)|cohesion]] consists of characteristics of the explicit text that play some role in helping the reader mentally connect ideas in the text."<ref name="graesser2003">{{Citation | last1 = Graesser | first1 = A.C. | last2 = McNamara | first2 = D.S. | last3 = Louwerse | first3 = M.M. | editor-last = Sweet | editor-first = A.P. | editor2-last = Snow | editor2-first = C.E. | year = 2003 | title = What do readers need to learn in order to process coherence relations in narrative and expository text | work = Rethinking reading comprehension | publisher = Guilford Publications | publication-place = New York | pages = 82–98}}</ref> The definition of coherence is the subject of much debate. Theoretically, the coherence of a text is defined by the interaction between linguistic representations and knowledge representations. While coherence can be defined as characteristics of the text (i.e., aspects of cohesion) that are likely to contribute to the coherence of the mental representation, Coh-Metrix measurements provide indices of these cohesion characteristics.<ref name="graesser2003" />

=== Artificial intelligence ===

Unlike the traditional readability formulas, [[artificial intelligence]] approaches to readability assessment (also known as Automatic Readability Assessment) incorporate myriad linguistic features and construct statistical prediction models to predict text readability.<ref name="Text Readability Assessment for Sec">{{cite journal |last1=Xia |first1=Menglin |last2=Kochmar |first2=Ekaterina |last3=Briscoe |first3=Ted |date=June 2016 |title=Text Readability Assessment for Second Language Learners |url=https://www.aclweb.org/anthology/W16-0502 |journal=Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications |pages=12–22 |arxiv=1906.07580 |doi=10.18653/v1/W16-0502 |doi-access=free}}</ref><ref name="aclweb.org">{{cite journal |last1=Lee |first1=Bruce W. |last2=Lee |first2=Jason |title=LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea |journal=Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications |date=Dec 2020 |pages=20–24 |doi=10.18653/v1/2020.nlptea-1.3 |arxiv=2010.13374 |url=https://www.aclweb.org/anthology/2020.nlptea-1.3}}</ref> These approaches typically consist of three steps: 1. a training corpus of individual texts, 2. a set of linguistic features to be computed from each text, and 3. a [[machine learning]] model to predict the readability, using the computed linguistic feature values.<ref>{{cite journal |last1=Feng |first1=Lijun |last2=Jansche |first2=Martin |last3=Huernerfauth |first3=Matt |last4=Elhadad |first4=Noémie |title=A Comparison of Features for Automatic Readability Assessment |journal=Coling 2010: Posters |date=August 2010 |pages=276–284 |url=https://www.aclweb.org/anthology/C10-2032}}</ref><ref name="On Improving the Accuracy of Readab">{{cite journal |last1=Vajjala |first1=Sowmya |last2=Meurers |first2=Detmar |title=On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition |journal=Proceedings of the Seventh Workshop on Building Educational Applications Using NLP |date=June 2012 |pages=163–173 |url=https://www.aclweb.org/anthology/W12-2019}}</ref><ref name="aclweb.org" />

In 2008, it was shown that syntactic complexity is correlated with longer processing times in text comprehension.<ref>{{cite journal |last1=Gibson |first1=Edward |title=Linguistic complexity: locality of syntactic dependencies |journal=Cognition |date=1998 |volume=68 |issue=1 |pages=1–76|doi=10.1016/S0010-0277(98)00034-1 |pmid=9775516 |s2cid=377292 }}</ref> It is common to use a rich set of these syntactic features to predict the readability of a text. The more advanced variants of syntactic readability features are frequently computed from [[parse tree]]. Emily Pitler ([[University of Pennsylvania]]) and Ani Nenkova (University of Pennsylvania) are considered pioneers in evaluating the parse-tree syntactic features and making it widely used in readability assessment.<ref>{{cite journal |last1=Pitler |first1=Emily |last2=Nenkova |first2=Ani |title=Revisiting Readability: A Unified Framework for Predicting Text Quality |journal=Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing |date=October 2008 |pages=186–195 |url=https://www.aclweb.org/anthology/D08-1020}}</ref><ref name="Computational assessment of text re" /> Some examples include:
*Average sentence length
*Average parse tree height
*Average number of noun phrases per sentence
*Average number of verb phrases per sentence

Lijun Feng proposed some cognitively-motivated features (mostly lexical) in 2009. This was during her [[doctorate]] study at the [[City University of New York]].<ref>{{cite book |last1=Feng |first1=Lijun |last2=Elhadad |first2=Noémie |last3=Huenerfauth |first3=Matt |chapter=Cognitively motivated features for readability assessment |title=Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on – EACL '09  |date=March 2009 |pages=229–237 |doi=10.3115/1609067.1609092 |s2cid=13888774 |chapter-url=https://dl.acm.org/doi/10.5555/1609067.1609092|doi-access=free }}</ref> The cognitively-motivated Although cognitively-motivated features were originally designed to ensure comprehension by adults with [[intellectual disability]], Feng showed such features also improved reading comprehension among the general population. In combination with a [[logistic regression]] model, cognitively-motivated features can correct the average error of [[Flesch–Kincaid readability tests|Flesch–Kincaid grade-level]] by more than 70%. The newly discovered features by Feng include:
*Number of [[lexical chain]]s in document
*Average number of unique entities per sentence
*Average number of entity mentions per sentence
*Total number of unique entities in document
*Total number of entity mentions in document
*Average lexical chain length
*Average lexical chain span

In 2012, Sowmya Vajjala at the [[University of Tübingen]] created the WeeBit corpus by combining educational articles from the [[Weekly Reader]] website and [[BBC Bitesize]] website, which provide texts for different age groups.<ref name="On Improving the Accuracy of Readab" /> In total, there are 3125 articles that are divided into five readability levels (from age 7 to 16). Weebit corpus has been used in several AI-based readability assessment research.<ref name="Computational assessment of text re">{{cite journal |last1=Collins-Thompson |first1=Kevyn |title=Computational assessment of text readability: A survey of current and future research |journal=International Journal of Applied Linguistics |date=2015 |volume=165 |issue=2 |pages=97–135|doi=10.1075/itl.165.2.01col |s2cid=17571866 }}</ref>

Wei Xu ([[University of Pennsylvania]]), Chris Callison-Burch ([[University of Pennsylvania]]), and Courtney Napoles ([[Johns Hopkins University]]) introduced the [[Newsela]] corpus to the academic field in 2015.<ref>{{cite journal |last1=Xu |first1=Wei |last2=Callison-Burch |first2=Chris |last3=Napoles |first3=Courtney |title=Problems in Current Text Simplification Research: New Data Can Help |journal=Transactions of the Association for Computational Linguistics |date=2015 |volume=3 |pages=283–297|doi=10.1162/tacl_a_00139 |s2cid=17817489 |doi-access=free }}</ref> The corpus is a collection of thousands of news articles professionally leveled to different reading complexities by professional editors at [[Newsela]].  The corpus was originally introduced for [[text simplification]] research, but was also used for text readability assessment.<ref>{{cite journal |last1=Deutsch |first1=Tovly |last2=Jasbi |first2=Masoud |last3=Shieber |first3=Stuart |title=Linguistic Features for Readability Assessment |journal=Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications |date=July 2020 |pages=1–17 |doi=10.18653/v1/2020.bea-1.1 |arxiv=2006.00377 |url=https://www.aclweb.org/anthology/2020.bea-1.1|doi-access=free }}</ref>

Advanced semantic or semantic features' influence on text readability was pioneered by Bruce W. Lee during his study at the ([[University of Pennsylvania]]), in 2021. Whilst introducing his features hybridization method, he also explored handcrafted advanced semantic features which aim to measure the amount of knowledge contained in a given text.<ref>{{cite book |last1=Lee |first1=Bruce W. |last2=Jang |first2=Yoo Sung |last3=Lee |first3=Jason Hyung-Jong |chapter=Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features |title=Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing |series=EMNLP '21 |date=November 2021 |pages=10669–10686 |doi=10.18653/v1/2021.emnlp-main.834 |s2cid=237940206 |chapter-url=https://aclanthology.org/2021.emnlp-main.834/|arxiv=2109.12258 }}</ref>
*Semantic Richness : <math>\sum_{i=1}^{n}  p_i \cdot i</math>
*Semantic Clarity : <math>\frac{1}{n} \cdot \sum_{i=1}^{n}  max(p) - p_{i}</math>
*Semantic Noise : <math>n \cdot \frac{\sum_{i=1}^{n} (p_i - \bar{p})^4}{(\sum_{i=1}^{n} (p_i - \bar{p})^2)^2}</math>
where the count of discovered topics (n) and topic probability (p)